Neural Pruning for 3D Scene Reconstruction: Efficient NeRF Acceleration
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913781151432704 |
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| author | Ding, Tianqi Xiang, Dawei Rivas, Pablo Dong, Liang |
| author_facet | Ding, Tianqi Xiang, Dawei Rivas, Pablo Dong, Liang |
| contents | Neural Radiance Fields (NeRF) have become a popular 3D reconstruction approach in recent years. While they produce high-quality results, they also demand lengthy training times, often spanning days. This paper studies neural pruning as a strategy to address these concerns. We compare pruning approaches, including uniform sampling, importance-based methods, and coreset-based techniques, to reduce the model size and speed up training. Our findings show that coreset-driven pruning can achieve a 50% reduction in model size and a 35% speedup in training, with only a slight decrease in accuracy. These results suggest that pruning can be an effective method for improving the efficiency of NeRF models in resource-limited settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00950 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Pruning for 3D Scene Reconstruction: Efficient NeRF Acceleration Ding, Tianqi Xiang, Dawei Rivas, Pablo Dong, Liang Computer Vision and Pattern Recognition Neural Radiance Fields (NeRF) have become a popular 3D reconstruction approach in recent years. While they produce high-quality results, they also demand lengthy training times, often spanning days. This paper studies neural pruning as a strategy to address these concerns. We compare pruning approaches, including uniform sampling, importance-based methods, and coreset-based techniques, to reduce the model size and speed up training. Our findings show that coreset-driven pruning can achieve a 50% reduction in model size and a 35% speedup in training, with only a slight decrease in accuracy. These results suggest that pruning can be an effective method for improving the efficiency of NeRF models in resource-limited settings. |
| title | Neural Pruning for 3D Scene Reconstruction: Efficient NeRF Acceleration |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.00950 |